Hello, I am a beginner with machine learning so please forgive me if this is a stupid question.

I’m trying to use a graph convolutional neural network to predict the classification of 3D data, specifically cell morphology. This is my testing method, where target is a one dimensional matrix of size n, n being the number of vertices.

`def test(model, test_loader, num_nodes, target, device):`

`model.eval()`

`correct = 0`

`total_loss = 0`

`n_graphs = 0`

`with torch.no_grad():`

`for idx, data in enumerate(test_loader):`

`out = model(data.to(device))`

`total_loss += F.nll_loss(out, target).item()`

`pred = out.max(1)[1]`

`correct += pred.eq(target).sum().item()`

`n_graphs += data.num_graphs`

`return correct / (n_graphs * num_nodes), total_loss / len(test_loader)`

How could I produce a single prediction for a piece of data instead of the tensor of predictions?